Run-up Model Research Articles

Coastal processes and dune stability: Insights from wave transmission and runup modeling

Dunes not only serve as natural landscapes but also act as crucial natural barriers protecting coastlines from storm surges. However, the stability of coastlines and dunes is further affected by climate change-induced sea level rise and increased storm activity. An effective evaluation of dune stability necessitates the collection of parameters such as wave height, changes in dune crest elevation, and dune erosion rates. This research employs wave flumes and high-speed camera technology in laboratory settings to observe and simulate coastal dynamics under overwash and collision regimes. The study investigates wave propagation under both regimes using the XBeach model. In the overwash regime, the model slightly overestimates the values of infragravity waves, whereas its accuracy improved under the collision regime. Moreover, in the overwash regime, higher wave skewness results in more sediment transport to the shore, exhibiting a linear relationship between sediment erosion volume and shoreline retreat distance. Through comparison with time series wave runup data obtained by cameras and traditional predictive formulas, the study validates the applicability of the formula proposed by Stockdon as a predictive tool for wave runup in this experiment. To evaluate dune stability, the study introduces the dimensionless overwash threshold parameter Cs, which is based on wave runup and dune crest elevation, to distinguish between dunes in a collision regime or an overwash regime. These findings help identify dune stability patterns, aiding in early detection of coastal erosion and assisting authorities in ecosystem management.

A high-performance, parallel, and hierarchically distributed model for coastal run-up events simulation and forecasting

The request for quickly available forecasts of intense weather and marine events impacting coastal areas is gradually increasing. High-performance computing (HPC) and artificial intelligence techniques are crucial in this application. Risk mitigation and coastal management must design scientific workflow appropriately and maintain them continuously updated and operational. Climate change accelerating increase trend of the past decades impacted on sea-level rise, together with broader factors such as geostatic effects and subsidence, reducing the effectiveness of coastal defenses. Due to this, the support tools, such as Early Warning Systems, have become increasingly more valuable because they can process data promptly and provide valuable indications for mitigation proposals. We developed the Shoreline Alert Model (SAM), an operational Python tool that produces simulation scenarios, ‘what-if’ assumptions, and coastal flooding forecasts to fill this gap in our study area. SAM aims to provide decision-makers, scientists, and engineers with new tools to help forecast significant weather-marine events and support related management or emergency responses. SAM aims to fill the gap between the wind-driven wave models, which produce simulations and forecasts of waves of significant height, period, and direction in deep or mid-water, and the run-up local models, which exstimulate marine ingression in the event of intense weather phenomena. It employs a parallelization scheme that allows users to run it on heterogeneous parallel architectures. It produced results approximately 24 times faster than the baseline when using shared memory with distributed memory, processing roughly 20,000 coastal cross-shore profiles along the coastline of the Campania region (Italy). Increasing the performance of this model and, at the same time, honoring the need for relatively modest HPC resources will enable the local manager and policymakers to enforce fast and effective responses to intense weather phenomena.

Leveraging successional facilitation to improve restoration of foundational dune grasses along a frequently disturbed coastline

Coastal ecosystems provide critical storm and flood protection but are rapidly degrading worldwide, making their restoration urgent. We evaluated whether successional facilitation, where pioneers facilitate climax species, could be leveraged to accelerate coastal dune revegetation. A survey spanning 270 km of Southeast U.S. coastline revealed that Panicum amarum (bitter panicum) supported higher plant richness than Uniola paniculata (sea oats) and that sea oat cover was 230% greater on mature dunes than disturbed dunes, suggesting bitter panicum functions as a pioneer and sea oats as a climax species. A reciprocal transplant experiment confirmed this interpretation: bitter panicum stem production and height fell by 37% and >20 cm, respectively, when planted proximate to sea oats versus in isolation, whereas sea oats produced 38% more and >12 cm taller stems when planted proximate to bitter panicum versus in isolation. A second experiment evaluating the density‐dependence of this facilitative interaction revealed that sea oats transplanted into low densities of bitter panicum grew >15% taller than isolated and high‐density treatments. However, within 7 months, wave inundation eliminated over 60% of propagules in both experiments. To explore foredune inundation frequency and its implications for dune revegetation, we applied empirical wave runup models at 101 locations throughout Volusia County, Florida. While disturbance frequency varied seasonally and annually, sites with low dune crests and steep beach slopes experienced frequent inundation (>50 events/year). Given the interactions between geomorphology and vegetation success, we present a decision matrix to guide managers in determining optimal revegetation methods tailored to project goals and site conditions.

Impact dynamics of granular debris flows based on a small-scale physical model

The peak pressure of a granular debris flow at low Froude conditions can be calculated with knowledge of the stress anisotropy and the bulk density as well as the run-up height at impact. Based on a small-scale physical model, measurements of stress anisotropy and flow density values at impact are presented and applied to existing run-up prediction models, and further compared with back-calculated run-up coefficients from measured maximum impact pressures. For this purpose, we conducted 17 experiments with impact measurements and six experiments without impact measurements at Froude numbers, ranging from 0.84 to 2.41. Our results indicate that run-up heights are best reproduced by predictive models, either based on energy or mass and moment conservation, when anisotropic stress conditions, found in this study to range from 1.2 to 5.0, and bulk density variations due to impact, ranging in this study from 0.8 to 2.3, are considered. The influence of stress anisotropy and density variation on the run-up prediction differs, depending on the modelling approach. For the calculation of run-up heights based on the energy conservation concept, the influence of stress anisotropy becomes more significant with increasing Froude number, whereas for models based on mass and momentum conservation, bulk density variations have a greater influence on the estimation of the potential run-up.

ADVANCES IN COASTAL FLOODING AND OVERTOPPING INVESTIGATIONS USING A 3D PHASE-RESOLVING WAVE MODEL

In engineering applications, runup and overtopping rates are often estimated from empirical parameterizations which rely on simplified assumptions of the incident wave conditions and can only be applied to a limited range of cross-shore profiles and simplified structural configurations. This study demonstrates the accuracy of DHI’s MIKE 3 Wave FM (M3WFM) model for runup and overtopping applications by comparison to physical model and prototype measurements. Subsequently the model has been used in more than six projects involving coastal flooding and overtopping investigations with applications in structural design, setback lines and response to climate change.

INVESTIGATION OF DIRECTIONAL SPREADING EFFECT ON WAVE RUN-UP USING SWASH

In order to keep optimal safety of coastal lines, it is necessary to maintain an appropriate crest level of coastal dikes. This is designed based on wave run-up height and/or wave overtopping discharge. Therefore, it is important to estimate wave run-up and overtopping as accurate as possible. However, the influence of directional spreading on wave run-up and overtopping has not been fully understood yet. In this study, non-hydrostatic model SWASH (Zijlema et al., 2011) is used. First, we implemented a wave run-up model in SWASH in 2DV (flume like) and 3D (basin like). Then wave run-up are simulated for some different cases and compared to the existing empirical wave run-up formulas in literature (e.g. Holman, 1986 and Mase, 1989).

Implications of second-order wave generation for physical modelling of force and run-up on a vertical wall using wave groups

Implications of second-order wave generation for physical modelling of force and run-up on a vertical wall using wave groups

Novel approach for predicting wave run-up on natural beaches

The evaluation of wave run-up on beaches is of significant importance since it is used in different coastal engineering and management applications, including the assessment of beach morphology, the determination of erosion and flooding risk, and the design and maintenance of coastal structures. The main aim of this paper is to present a new method of wave run-up prediction, which is intended to be applicable to the various types of natural beaches. A single formulation was established for beaches, which were categorised as dissipative, reflective and intermediate. This comprehensive parametric run-up model, which included both wave set-up and swash excursion, was specifically developed to represent the wide-ranging conditions experienced in the swash zone in terms of beach conditions. The run-up model included a means to account for obliquely incident waves, varying foreshore sediment characteristics and the infiltration/exfiltration processes during swash events; it also used fitting parameters to account for other site-specific conditions. The novel run-up model produced comparable results to other parametric models, and showed good correlation with laboratory data and limited field data. However, while the model was deemed suitable to be adopted at varying coastal sites, field data are required for implementation to yield meaningful output.

An optimized XGBoost-based machine learning method for predicting wave run-up on a sloping beach

Accurate and computationally efficient prediction of wave run-up is required to mitigate the impacts of inundation and erosion caused by tides, storm surges, and even tsunami waves. The conventional methods for calculating wave run-up involve physical experiments or numerical modeling. Machine learning methods have recently become a part of wave run-up model development due to their robustness in dealing with large and complex data. In this paper, an extreme gradient boosting (XGBoost)-based machine learning method is introduced for predicting wave run-up on a sloping beach. More than 400 laboratory observations of wave run-up were utilized as training datasets to construct the XGBoost model. The hyperparameter tuning through the grid search approach was performed to obtain an optimized XGBoost model. The performance of the XGBoost method is compared to that of three different machine learning approaches: multiple linear regression (MLR), support vector regression (SVR), and random forest (RF). The validation evaluation results demonstrate that the proposed algorithm outperforms other machine learning approaches in predicting the wave run-up with a correlation coefficient (R2) of 0.98675, a mean absolute percentage error (MAPE) of 6.635%, and a root mean squared error (RMSE) of 0.03902. Compared to empirical formulas, which are often limited to a fixed range of slopes, the XGBoost model is applicable over a broader range of beach slopes and incident wave amplitudes.•The optimized XGBoost method is a feasible alternative to existing empirical formulas and classical numerical models for predicting wave run-up.•Hyperparameter tuning is performed using the grid search method, resulting in a highly accurate machine-learning model.•Our findings indicate that the XGBoost method is more applicable than empirical formulas and more efficient than numerical models.

Safety Analysis of a Nuclear Power Plant against Unexpected Tsunamis

The East Sea (Sea of Japan), surrounded by Korea, Japan, and Russia, is highly vulnerable to catastrophic tsunamis. Several nuclear power plants (NPPs) operate along the eastern coast of Korea and several more are under construction. Unexpected tsunamis can affect these power plants. The safety of NPPs has attracted worldwide attention since the Fukushima NPP accident. In this study, a coupled numerical model comprising propagation and run-up models was employed to investigate the safety of an NPP against unexpected tsunami attacks. The maximum and minimum tsunami heights and arrival times of the leading tsunami were numerically predicted to ensure the safety of the Uljin NPP, where six plants are already operational and two more are under construction. The predicted numerical results were compared with the safety guidelines proposed by relevant authorities. These results indicate that NPPs are reasonably safe from unexpected tsunamis. Additionally, we confirmed that the tsunami heights and arrival times of a leading tsunami becomes smaller and delayed as the latitude of the epicenter increased.

Assessing the accuracy of Sentinel-2 instantaneous subpixel shorelines using synchronous UAV ground truth surveys

Due to recent technological advancements in the field of cloud-based satellite remote sensing, the barriers to global analysis ready dataset access and processing have lowered drastically increasing the opportunity for large-scale long-term satellite-derived shoreline monitoring solutions. However, validation studies employing synchronous and independent in-situ instantaneous shorelines at scale are limited. Thanks to a unique dataset of 47 synchronous UAV photogrammetric surveys in 15 coastal locations in Victoria, Australia, over 3 years, we validated Sentinel-2 instantaneous subpixel shorelines extracted using the most common water indices, CoastSat and a tidal-balanced convolutional neural network (Unet+++). We assessed the accuracy of the water level estimation using 9 wave run-up models and wave data sourced from a nearshore wave buoy network, a regional hindcast model and a global altimeter wave dataset. This paper also presents the first validation of Deep Learning-derived shorelines using synchronous independent in-situ data. We found the best overall performance and least threshold-sensitive shoreline extraction approach to be the WI index, whilst NDWI had the poorest performances despite being the most utilized across the literature. We found that the thresholdless Unet+++ solution coupled with an ensemble wave run-up model and the altimeter wave data is an adequate trade-off in terms of accuracy, adaptability and wave data access for global scale shoreline studies.Moreover, we highlighted the very high spatial variability of beach-scale shoreline extraction performances and found that generally, the best SDS extractor only performs best along 30–40% of a coastline. Our results suggest that future satellite-derived shoreline approaches should investigate the relationships between scene-dependent variables and shoreline accuracies in order to better understand the causes of beach-scale performance variations and increase the accuracy of satellite-derived shorelines.

Assessment and optimisation of runup formulae for beaches fronted by fringing reefs based on physical experiments

An extensive new set of laboratory measurements have been performed with random and monochromatic waves to study runup on fringing reef-fronted beaches. The experiments were conducted with an idealised fringing reef profile and tested with different forcing conditions (waves and water levels) and reef geometries. Experimental physical data are compared with empirical models developed on open coasts to predict runup. The results show that runup predictions are scattered and are overestimated by existing empirical models when using the off-reef wave conditions. Predictions based on the wave and water level conditions at the beach toe show much less scatter, with the run-up scaling remaining consistent with that proposed by Hunt (1959). Two revised optimizations of Hunt (1959) formula were derived from the experimental data. The results showed that these formulations and the existing runup scaling laws are useful to characterize runup on fringing reef-fronted beaches if the appropriate wave and water level conditions are adopted. Consequently, existing runup models can be combined with the results from phase-averaging wave models to estimate runup on beaches landward of fringing reefs.

Improvement of empirical formulas to estimate the reduction effects by berms on irregular wave runup over a dune-berm coast

This study formulates the reduction effects of a sandy berm on irregular wave runup over a dune-berm coast. The numerical experiments by Park and Cox (2016) are closely re-examined to develop an empirical formula describing the variability of reduction effects of a sandy berm over a broad range of conditions. Based on a sequence of regression analyses, the reduction effects are expressed as a reduction factor in terms of normalized berm width, normalized surge level, and wave steepness in deep water. The comparison with the numerical experiments demonstrates that the regression formula can satisfactorily reproduce the variability of the reduction effects over the range of numerical experiments. The analysis of prediction uncertainty demonstrated that the derived formula reproduced the reduction effects observed in the numerical experiments with negligible bias and a 90% confidence interval of approximately ±20% relative error. In addition, conversion formulas between representative runup values based on different statistical definitions are derived to enable consistent comparisons between them. The proposed reduction formula is implemented into three empirical runup models that are applicable to the quick estimations of irregular wave runup on a dune-berm coast: the models by Park and Cox (2016), Stockdon et al. (2006), and Mase et al. (2013). Consistent comparisons were conducted among the empirical predictions and numerical experiments based on the statistical conversion formulas. Combined with the proposed reduction formula, all three models well reproduced the normalized 2% runup in the numerical experiments over a wide range of conditions. On the other hand, the uncertainty in the runup prediction appeared in different forms depending on the selected model. When the proposed reduction formula was implemented in the modified Park and Cox (2016) and modified Stockdon et al. (2006) models, the uncertainty was described by a log-normal distribution of the error ratio between the empirical predictions and numerical experiments. Quantitatively, these two models predicted 90% of the normalized runup on a dune within a range of relative error of less than approximately 20–30%. When the proposed reduction formula was combined with the model by Mase et al. (2013), the uncertainty followed a normal distribution of the residual error between the empirical predictions and numerical experiments. On the normalized runup, the model prediction indicated a small conservative bias (+0.05) and a root-mean-square error of 0.13.

PENENTUAN LOKASI DAN JALUR EVAKUASI TSUNAMI DI KABUPATEN CIANJUR JAWA BARAT

The villages of Sukapura, Cisalak, and Jayapura in Cidaun Subdistrict, Cianjur Regency, West Java are three of the 584 villages on the south coast of Java which are very prone to tsunamis with a high hazard class. In the 2011-2031 Cianjur Regency Spatial Plan, the area designated as the site and route for tsunami evacuation is very far from the villages of Sukapura, Cisalak and Jayapura. The purpose of this study was to identify the site and route of tsunami evacuation in the three village areas. The method used in this research is tsunami run-up modeling with a model builder to measure the tsunami inundation area. Determination of tsunami evacuation sites uses overlay analysis, while determining tsunami evacuation routes uses network analysis. The results of this study, there are 17 tsunami evacuation sites and 5 tsunami evacuation routes to the nearest evacuation location in each village area. The research is expected to provide an overview for the local community and advice for the government in order to increase awareness and mitigate tsunami disasters.

The Modeling of Tsunami Wave Run-Up and Vulnerability Zone Analysis In Cipatujah, Tasikmalaya District

Cipatujah is a sub-district in Tasikmalaya Regency, West Java Province, Indonesia, which is one of the areas affected by significant damage due to tectonic activity in the southern region of Java, namely an earthquake with a magnitude of 7.7 magnitude and West Java tsunami in 2006 after Nusa Kambangan and PangandaranThe purpose of this study is to estimate the tsunami travel time, wave heights distribution, coverage areas, and vulnerability zone based on the Java 17 July 2006 earthquake scenario in Cipatujah, Tasikmalaya Regency. The method used in this study is quantitative and requires bathymetry data, high points, earthquake parameters of July 17, 2006, tidal data, RBI maps, and land use data. Tsunami modeling with numerical simulations using COMCOT v 1.7 and MATLAB software, also Arc Map for mapping tsunami coverage and vulnerability zone areas. Based on data processing, it was found that the maximum wave speed of 3.92 m/s. In the 27th minute, the waves had reached the Cipatujah region, with a maximum run-up height of 6.115 meters and a maximum tsunami range on the land of 1.891 meters. The area affected by the tsunami based on the processing of tsunami coverage data in Cipatujah totals 11.64 km2. Whereas for tsunami vulnerability zone in Cipatujah, it is divided into 3 categories, namely low hazard zone, medium hazard zone, and high hazard zone. This indicates that the Cipatujah region is included in a high tsunami-prone zone. Based on the results of verification and validation with an RSR value of 0,073, then it is assumed that the height of the run-up model is sufficient by the run-up data that occurred during the event.

Numerical modelling of wave transformation and runup over rough fringing reefs using VARANS equations

Computational Fluid Dynamics (CFD) models are gaining popularity recently in efforts to understand the complex dynamics of wave interaction with coral reef profiles. To provide an alternative approach to the current CFD models to account for the reef surface with large bottom roughness, a 3D numerical wave tank based on the CFD tool OpenFOAM® is developed in this study. The VARANS equations are solved for two-phase incompressible flow with the k-ω SST model for the turbulence closure and VOF method for tracking the free surface. The reef surface with high friction is modeled by using a porous media model in the VARANS equations. The numerical model is first validated with laboratory experiments. The model is then applied to evaluate the impacts of forcing condition (wave and water level), reef morphology (reef-flat width, fore-reef slope, and back-reef slope), and reef roughness on wave transformation and wave runup. Moreover, an empirical formula is derived from the numerical results to predict wave runup. Subsequently, the simulated cross-reef variations of energy dissipation, wave spectrum, wave shape parameters (skewness and asymmetry) and nonlinearity parameter (Ursell number) are discussed for both the smooth and rough reefs. Finally, the cross-reef variations of the TKE and its dissipation rate are also examined for both reefs via the numerical simulations.

Modeling of tsunami run-up using terrain model data based on photogrammetry processing product (case study at Way Muli Village, Rajabasa, South Lampung)

Photogrammetry has become a trend in large-scale mapping today. The ability to produce large-scale geospatial products in a relatively short time and low cost is very beneficial for mapping. The ability of high temporal and spatial resolution also makes photogrammetry used in the disaster mapping process. In this study, the DEM approach from photogrammetry was used for input data in tsunami run-up modeling activities in Way Muli Village. High temporal and spatial capabilities are utilized for the production of surface roughness and elevation which are key parameters for rigorous inundation modeling. The modeled inundation results show that the run-up limit achieved in residential areas is on the main road with a maximum distance of inundation from the shoreline is an average of 80 m. The results obtained can be used by the village government to preparedness in dealing with the tsunami.

Kompleksnoye issledovaniye volnovykh i litodinamicheskikh protsessov v beregovoy zone p. Morskoye (Vostochnyy Krym)

Wind waves can have a significant impact on the coastal infrastructure. The paper aims at a comprehensive study of regional characteristics of wind waves near the village of Morskoye (south-eastern coast of Crimea), which are necessary to develop a project of reconstruction of the highway adjacent to the coastal area. Space images and cartographic information were used to study the beach dynamics in the studied area. It is shown that before construction of the coast protection structures the beach width in the studied area was 25–30 m, whereas after the construction it narrowed down to 15–25 m. Based on the wind wave reanalysis data obtained using SWAN spectral model and ERA-Interim surface wind fields for 1979–2017, regime characteristics of waves in the coastal zone of Morskoye were calculated. It was found that waves with average periods of 3.0–3.5 s have the maximum recurrence (over 16 %). Wind waves coming from SE-SSE sector have the highest recurrence rate. Estimates were obtained for the extreme characteristics of wind waves that may occur once in a given number of years. The SWASH hydrodynamic model was used to perform mathematical modelling of wave run-up on the coastal area. In their calculations the authors used a regular grid of the coastal relief with high spatial resolution based on the interpolation of topo-geodetic and bathymetric survey results. An incoming wave was given as a soliton of 2.0; 3.0 and 3.4 m high. It was found that with the incoming wave height of 2.0 m, the vertical wave splash in the studied area varies within 1.7–2.2 m. At a height of 3.4 m, the splash reaches 1.8–2.9 m. In this case the beach is flooded completely. During the run-up, wave current velocity amounts up to 5 m/s. Along the lower boundary of the cliff the bottom maximum current velocity reaches 1.5–1.75 m/s. At such velocities near the cliff, the beach consisting of material with the grain size up to 60–90 mm can be eroded.

Exposure of Loggerhead Sea Turtle Nests to Waves in the Florida Panhandle

Wave wash-over poses a significant threat to sea turtle nests, with sustained exposure to waves potentially resulting in embryonic mortality and altered hatchling locomotor function, size, and sex ratios. Identifying where and under what conditions wave exposure becomes a problem, and deciding what action(s) to take (if any), is a common issue for sea turtle managers. To determine the exposure of sea turtle nests to waves and identify potential impacts to hatchling productivity, we integrated a geographic information system with remote sensing and wave runup modeling across 40 nesting beaches used by the Northern Gulf of Mexico Loggerhead Recovery Unit. Our models indicate that, on average, approximately 50% of the available beach area and 34% of nesting locations per nesting beach face a significant risk of wave exposure, particularly during tropical storms. Field data from beaches in the Florida Panhandle show that 42.3% of all nest locations reported wave exposure, which resulted in a 45% and 46% decline in hatching and emergence success, respectively, relative to their undisturbed counterparts. Historical nesting frequency at each beach and modeled exposure to waves were considered to identify priority locations with high nesting density which either experience low risk of wave exposure, as these are good candidates for protection as refugia for sustained hatchling production, or which have high wave exposure where efforts to reduce impacts are most warranted. Nine beaches in the eastern Florida Panhandle were identified as priority sites for future efforts such as habitat protection or research and development of management strategies. This modeling exercise offers a flexible approach for a threat assessment integration into research and management questions relevant to sea turtle conservation, as well as for other beach species and human uses of the coastal environment.

An early warning system for wave-driven coastal flooding at Imperial Beach, CA

Waves overtop berms and seawalls along the shoreline of Imperial Beach (IB), CA when energetic winter swell and high tide coincide. These intermittent, few-hour long events flood low-lying areas and pose a growing inundation risk as sea levels rise. To support city flood response and management, an IB flood warning system was developed. Total water level (TWL) forecasts combine predictions of tides and sea-level anomalies with wave runup estimates based on incident wave forecasts and the nonlinear wave model SWASH. In contrast to widely used empirical runup formulas that rely on significant wave height and peak period, and use only a foreshore slope for bathymetry, the SWASH model incorporates spectral incident wave forcing and uses the cross-shore depth profile. TWL forecasts using a SWASH emulator demonstrate skill several days in advance. Observations set TWL thresholds for minor and moderate flooding. The specific wave and water level conditions that lead to flooding, and key contributors to TWL uncertainty, are identified. TWL forecast skill is reduced by errors in the incident wave forecast and the one-dimensional runup model, and lack of information of variable beach morphology (e.g., protective sand berms can erode during storms). Model errors are largest for the most extreme events. Without mitigation, projected sea-level rise will substantially increase the duration and severity of street flooding. Application of the warning system approach to other locations requires incident wave hindcasts and forecasts, numerical simulation of the runup associated with local storms and beach morphology, and model calibration with flood observations.